Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This is a Final Office Action, in responses to Applicans’ amendments/remarks filed 06/25/2026. It is noted, the current Patent Application was originally filed 05/09/2023; is a Continuation of 15494971, filed 04/24/2017, now U.S. Patent # 11797837; issued 10/24/2023. Claim(s) 1-20 are pending. Claim(s) 1, 9 and 17 is/are independent. Claim(s) 1-20 were original. Also, Examiner is acknowledged the Applicants amendments to the specifications filed 06/25/2026.
In addition, in the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
A signed and dated copy of applicant’s IDS, which was filed 05/01/2026 is/are attached to this Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter) includes... “a graphics processor comprising: a memory device; a graphics processing cluster coupled with the memory device, the graphics processing cluster including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors “interconnected via a data interconnect”, wherein a graphics multiprocessor of the plurality of graphics multiprocessors includes circuitry” configured to “load a modular neural network” including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject, the “graphics multiprocessor” configured to:
“ load weights associated with a baseline set of layers of the modular neural network” to the memory device;
determine a first subject associated with a deployment environment;
“load weights for a first subnetwork to the memory device”, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image “captured within the deployment environment”, wherein the first image includes the first subject ...and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... ... “a graphics processor comprising: a memory device; a graphics processing cluster coupled with the memory device, the graphics processing cluster including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors “interconnected via a data interconnect”, wherein a graphics multiprocessor of the plurality of graphics multiprocessors includes circuitry” configured to “load a modular neural network” including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject, the “graphics multiprocessor” configured to:
“ load weights associated with a baseline set of layers of the modular neural network” to the memory device;
determine a first subject associated with a deployment environment;
“load weights for a first subnetwork to the memory device”, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image “captured within the deployment environment”, wherein the first image includes the first subject... ...These limitation(s) recite mathematical calculation; since the training of the machine model; to recognize the first subject; and perform a first matrix operation...then “Apply it” (i.e. load weights associated with a baseline set of layers of the modular neural network...) (as describes in US 20230334316 A1. Para 147; i.e., the machine learning application can be configured to perform the necessary computations using the primitives provided by the machine learning framework ...which are computational operations that are performed while training a convolutional neural network (CNN). The machine learning framework can also provide primitives to implement basic linear algebra subprograms performed by many machine-learning algorithms, such as matrix and vector operation...)..., “APPLY IT” (the weights associated with a baseline set of layers of the modular neural network” to the memory device).
Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as: ... “graphics multiprocessor/ memory device ” ... to: load weights associated with a baseline set of layers of the modular neural network” to the memory device; that determine a first subject associated with a deployment environment; and“ load weights for a first subnetwork to the memory device, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment......it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application (see the US 20230334316 A1. Para 3 ; i.e., Parallel graphics processors with single instruction, multiple thread (SIMT) architectures are designed to maximize the amount of parallel processing in the graphics pipeline. In an SIMT architecture, groups of parallel threads attempt to execute program instructions synchronously together as often as possible to increase processing efficiency. The efficiency provided by parallel machine learning algorithm implementations allows the use of high-capacity networks and enables those networks to be trained on larger datasets) ... to load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation...
These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... ... “graphics multiprocessor/ memory device ” to: load weights associated with a baseline set of layers of the modular neural network” to the memory device; that determine a first subject associated with a deployment environment; and“ load weights for a first subnetwork to the memory device, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment...These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-8, 10-16 and 18-20, further recite, addition limitation(s) such as, (dynamically load weights for a second subnetwork, perform a second matrix operation, second image includes the second subject, output associated with the baseline set of layers of the modular neural network is provided as input the first subnetwork to perform the first computer vision operation and provided as input the second subnetwork to perform the second computer vision operation, apply a first priority to the first subnetwork; apply a second priority to the second subnetwork; and adjust a resource allocation within the graphics processing cluster based on the first priority and the second priority, load first optical flow data associated with a first sequence of images, perform a fourth matrix operation, facilitate detection of a second set of stationary objects of the second subject, second optical flow data includes dense optical flow data, determine velocities of moving objects, assign a hazard associated with the moving objects and second set of stationary objects based on a velocity and trajectory of the moving objects...etc.), These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
Claims Rejection – 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 9-12 and 17-20 rejected under 35 U.S.C. 103 as being unpatentable over Haruki et al., (“US 20180121806 A1” filed 02/03/2017 [hereinafter “Haruki”], in view of Kanno et al., (“US 20180260687 A1” filed 04/26/2016 [hereinafter “Kanno”].
Independent Claim 1, Haruki teaches: A graphics processor comprising: a memory device; a graphics processing cluster coupled with the memory device, the graphics processing cluster including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors interconnected via a data interconnect, wherein a graphics multiprocessor of the plurality of graphics multiprocessors, (In Haruki the Abstract and Para(s) 2 and 5, discloses the efficient parallel training of a neural network model on multiple graphics processing units; wherein the Artificial neural networks (ANNs) are computational models and are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. ANNs generally include sets of adaptive weights, i.e., numerical parameters that are tuned by a learning algorithm. The adaptive weights are, conceptually, connection strengths between “neurons,” which are activated during training and prediction...) Haruki further teaches: includes circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject, (Haruki in Para(s) 3-4 and 19-20, discloses Deep Neural Networks (DNNs) typically incorporate large models trained on big datasets.... the Training is often accelerated by using graphics processing units (GPUs) and parallelizing the training with data parallelism. This is particularly effective for convolutional neural networks. The layers in convolutional neural networks usually start with convolutional layers having a small number of parameters and end with fully connected layers having a large number of parameters.... it is noted; the convolutional networks have wide applications in image and video recognition. ...) Haruki further teaches, determine a first subject associated with a deployment environment; (In Haruki Para 18 , discloses deep learning using a convolutional neural network is an effective tool for solving complex problems in computer vision, speech recognition, and natural language processing. For example, deep learning has been successfully used to recognize objects in digital images....)
It is noted that Haruki discloses a method of efficient parallel training of a neural network model on multiple graphics processing units; wherein the training module collects gradients of multiple layers during backpropagation of training from a plurality of graphics processing units (GPUs),... However, Haruki does not expressly teach, But the combination of Haruki and Kanno teach,... the graphics multiprocessor configured to: load weights associated with a baseline set of layers of the modular neural network to the memory device;... load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment, wherein the first image includes the first subject...(In Kanno the Abstract and Para(s) 21-24, describing a method where a unit that performs an operation on data of a second layer using data of a first layer and performs an operation on data of the first layer using data of the second layer in a multi-layered neural network . It is the weight data of deciding a relation between each piece of data of the first layer and each piece of data of the second layer in both the operations ... and the weight data is stored in one storage holding unit as all weight coefficient matrices to be constructed. Further, an operation unit including product-sum operators which are constituent elements of the weight coefficient matrix and correspond to operations of matrix elements in a one-to-one manner is provided, and when the matrix elements constituting the weight coefficient matrix are stored in the storage holding unit, the matrix elements are stored using a row vector of the matrix as a basic unit, and the operation of the weight coefficient matrix is performed in basic units in which the storage is performed in the storage holding unit...Moreover, in a case in which the data of the first layer is calculated from the data of the second layer using the weight coefficient matrix, the data of the second layer is arranged similarly to the column vector of the matrix, and each element is input to the product-sum operator, at the same time, a first row of the weight coefficient matrix is input to the product-sum operator, a multiplication operation related to both pieces of data is performed, and an operation result is stored in the accumulator, when second or less rows of the weight coefficient matrix are calculated, the data of the second layer is shifted to the left or the right each time a row operation of the weight matrix is performed, and then a multiplication operation of element data of a corresponding row of the weight coefficient matrix and the arranged data of the second layer is performed, then, data stored in the accumulator of the same operation unit is added, and a similar operation is performed up to an N-th row of the weight coefficient matrix is provide ... Also, in Kanno Para(s) 138-141, further mentions the same target is imaged through a plurality of cameras, and an image recognition process is executed. Since a video captured by a camera 1 and a video captured by a camera 2 differ in position, the shapes of the subject are different although the same subject is imaged. Therefore, it is efficient since it is possible to acquire information at the same time under different conditions such as a photographing angle or a radiation degree of light rays and perform the recognition and the learning...utilizing the weight coefficient matrix is input to the product-sum operator as describes in Para(s) 21-24., ..)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki’s parallel training of a neural network model on multiple graphics processing units, to include a means said the graphics multiprocessor configured to: load weights associated with a baseline set of layers of the modular neural network to the memory device;... load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment, wherein the first image includes the first subject AS TAUGHT BY Kanno, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 2, Haruki and Kanno further teach: the graphics multiprocessor configured to: dynamically load weights for a second subnetwork to the memory device, the second subnetwork trained to recognize a second subject associated with the deployment environment; and perform a second matrix operation associated with the second subnetwork to facilitate a second computer vision operation on a second image captured within the deployment environment, wherein the second image includes the second subject; In Kanno the Abstract and Para(s) 21-24, describing a method where a unit that performs an operation on data of a second layer using data of a first layer and performs an operation on data of the first layer using data of the second layer in a multi-layered neural network . It is the weight data of deciding a relation between each piece of data of the first layer and each piece of data of the second layer in both the operations ... and the weight data is stored in one storage holding unit as all weight coefficient matrices to be constructed. Further, an operation unit including product-sum operators which are constituent elements of the weight coefficient matrix and correspond to operations of matrix elements in a one-to-one manner is provided, and when the matrix elements constituting the weight coefficient matrix are stored in the storage holding unit, the matrix elements are stored using a row vector of the matrix as a basic unit, and the operation of the weight coefficient matrix is performed in basic units in which the storage is performed in the storage holding unit...Moreover, in a case in which the data of the first layer is calculated from the data of the second layer using the weight coefficient matrix, the data of the second layer is arranged similarly to the column vector of the matrix, and each element is input to the product-sum operator, at the same time, a first row of the weight coefficient matrix is input to the product-sum operator, a multiplication operation related to both pieces of data is performed, and an operation result is stored in the accumulator, when second or less rows of the weight coefficient matrix are calculated, the data of the second layer is shifted to the left or the right each time a row operation of the weight matrix is performed, and then a multiplication operation of element data of a corresponding row of the weight coefficient matrix and the arranged data of the second layer is performed, then, data stored in the accumulator of the same operation unit is added, and a similar operation is performed up to an N-th row of the weight coefficient matrix is provide ... Also, in Kanno Para(s) 138-141, further mentions the same target is imaged through a plurality of cameras, and an image recognition process is executed. Since a video captured by a camera 1 and a video captured by a camera 2 differ in position, the shapes of the subject are different although the same subject is imaged. Therefore, it is efficient since it is possible to acquire information at the same time under different conditions such as a photographing angle or a radiation degree of light rays and perform the recognition and the learning...utilizing the weight coefficient matrix is input to the product-sum operator as describes in Para(s) 21-24., ...)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki’s parallel training of a neural network model on multiple graphics processing units, to include a means said dynamically load weights for a second subnetwork to the memory device, the second subnetwork trained to recognize a second subject associated with the deployment environment; and perform a second matrix operation associated with the second subnetwork to facilitate a second computer vision operation on a second image captured within the deployment environment, wherein the second image includes the second subject AS TAUGHT BY Kanno, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 3, Haruki and Kanno further teach: wherein the graphics multiprocessor is configured such that output associated with the baseline set of layers of the modular neural network is provided as input the first subnetwork to perform the first computer vision operation and provided as input the second subnetwork to perform the second computer vision operation; (In Kanno the Abstract and Para(s) 21-24, describing a method where a unit that performs an operation on data of a second layer using data of a first layer and performs an operation on data of the first layer using data of the second layer in a multi-layered neural network . It is the weight data of deciding a relation between each piece of data of the first layer and each piece of data of the second layer in both the operations ... and the weight data is stored in one storage holding unit as all weight coefficient matrices to be constructed. Further, an operation unit including product-sum operators which are constituent elements of the weight coefficient matrix and correspond to operations of matrix elements in a one-to-one manner is provided, and when the matrix elements constituting the weight coefficient matrix are stored in the storage holding unit, the matrix elements are stored using a row vector of the matrix as a basic unit, and the operation of the weight coefficient matrix is performed in basic units in which the storage is performed in the storage holding unit...Moreover, in a case in which the data of the first layer is calculated from the data of the second layer using the weight coefficient matrix, the data of the second layer is arranged similarly to the column vector of the matrix, and each element is input to the product-sum operator, at the same time, a first row of the weight coefficient matrix is input to the product-sum operator, a multiplication operation related to both pieces of data is performed, and an operation result is stored in the accumulator, when second or less rows of the weight coefficient matrix are calculated, the data of the second layer is shifted to the left or the right each time a row operation of the weight matrix is performed, and then a multiplication operation of element data of a corresponding row of the weight coefficient matrix and the arranged data of the second layer is performed, then, data stored in the accumulator of the same operation unit is added, and a similar operation is performed up to an N-th row of the weight coefficient matrix is provide ... Also, in Kanno Para(s) 138-141, further mentions the same target is imaged through a plurality of cameras, and an image recognition process is executed. Since a video captured by a camera 1 and a video captured by a camera 2 differ in position, the shapes of the subject are different although the same subject is imaged. Therefore, it is efficient since it is possible to acquire information at the same time under different conditions such as a photographing angle or a radiation degree of light rays and perform the recognition and the learning...utilizing the weight coefficient matrix is input to the product-sum operator as describes in Para(s) 21-24., ..)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki’s parallel training of a neural network model on multiple graphics processing units, to include a means said output associated with the baseline set of layers of the modular neural network is provided as input the first subnetwork to perform the first computer vision operation and provided as input the second subnetwork to perform the second computer vision operation AS TAUGHT BY Kanno, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 4, Haruki and Kanno further teach: the graphics multiprocessor configured to: apply a first priority to the first subnetwork; apply a second priority to the second subnetwork; and adjust a resource allocation within the graphics processing cluster based on the first priority and the second priority; ( In Kanno Para(s) 106-107 and FIG. 7, illustrates a data operation technique in efficiently operating the hierarchy type DNN system in a as a FIFO order from an upper-level hierarchy to a lower-level hierarchy (DNN1, DNN2....DNNn, and so on...) if the recognition score information obtained by performing the recognition process in the DNN1 and the neural network configuration information and the weight coefficient information of the DNN1 device are simultaneously stored, the efficiency is good when additional learning is performed in the second hierarchy machine learning/recognizing device DNN2...and so... (in the BRI, is recognized as adjust a resource allocation within the graphics processing cluster based on the first priority and the second priority...as claimed.)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki’s parallel training of a neural network model on multiple graphics processing units, to include a means said apply a first priority to the first subnetwork; apply a second priority to the second subnetwork; and adjust a resource allocation within the graphics processing cluster based on the first priority and the second priority AS TAUGHT BY Kanno, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Regarding Claim(s) 17-20 (respectively) is/are fully incorporated similar subject of claim(s) 1-4 (respectively) cited above.
Claim(s) 5-8 and 13-16 rejected under 35 U.S.C. 103 as being unpatentable over Haruki et al., (“US 20180121806 A1” filed 02/03/2017 [hereinafter “Haruki”], in view of Kanno et al., (“US 20180260687 A1” filed 04/26/2016 [hereinafter “Kanno”], and further in view of MAHMOUDI et al., NPL (“Real-time motion tracking using optical flow on multiple GPUs”) Published 2014 – 12 pages (pages 139-150) [[hereinafter “Mahmoodi”],
Claim 5, It is noted, the GPUs of Haruki and Kanno do not expressly teach, but the combination of Haruki and Kanno and Mahmoudi teach the limitations said,... load first optical flow data associated with a first sequence of images that includes the first image; and perform a third matrix operation associated with the first subnetwork to facilitate detection of a first set of stationary objects of the first subject, the third matrix operation performed based on the first optical flow data and the first sequence of images; (In Mahmoudi Pages 139 -146 begins section 4. “Motion tracking algorithm”, discloses the GPUs-based motion tracking using the optical flow and matrix operation performed based on the optical flow data and the sequence of images/video.( set of stationary objects such as located on the static objects like trees or a building... .)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki/Kanno’s parallel training of a neural network model on multiple graphics processing units, to include a means said ,... load first optical flow data associated with a first sequence of images that includes the first image;... , the third matrix operation performed based on the first optical flow data and the first sequence of images.. AS TAUGHT BY Mahmoudi, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 6, Haruki and Kanno and Mahmoudi further teach: the graphics multiprocessor configured to: load second optical flow data associated with a second sequence of images that includes the second image; and perform a fourth matrix operation associated with the second subnetwork to facilitate detection of a second set of stationary objects of the second subject, ...(In Kanno Para 142, further mentions a machine learning system using different sensors (for example, a camera and a microphone..(, i.e., detecting set of stationary objects of the first/second/third/fourth...N.. subject) ..Moreover Kanno Para(s) 21-24, mentions an operation unit including product-sum operators which are constituent elements of the weight coefficient matrix and correspond to operations of matrix elements in a one-to-one manner is provided, and when the matrix elements constituting the weight coefficient matrix are stored in the storage holding unit, the matrix elements are stored using a row vector of the matrix as a basic unit, and the operation of the weight coefficient matrix is performed in basic ...i.e., perform a first second, third, fourth...n... matrix operation associated with the ANNs..)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki and Mahmoodi’s multiple graphics processing units, to include a means said load second optical flow data associated with a second sequence of images that includes the second image; and perform a fourth matrix operation associated with the second subnetwork to facilitate detection of a second set of stationary objects of the second subject... AS TAUGHT BY Kanno, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
It is noted, the GPUs of Haruki and Kanno do not expressly teach, but the combination of Haruki and Kanno and Mahmoudi teach the limitations said the fourth matrix operation performed based on the second optical flow data and the second sequence of images; (In Mahmoudi Pages 139 -146 begin section 4. “Motion tracking algorithm”, discloses the GPUs-based motion tracking using the optical flow and matrix operation performed based on the optical flow data and the sequence of images/video...)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki/Kanno’s parallel training of a neural network model on multiple graphics processing units, to include a means said , ... the fourth matrix operation performed based on the second optical flow data and the second sequence of images ... AS TAUGHT BY Mahmoudi, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 7, Haruki and Kanno and Mahmoudi further teach: wherein the first optical flow data and the second optical flow data includes dense optical flow data, (In Mahmoudi Pages 131 -146 begin section 4. “Motion tracking algorithm”, discloses the GPUs-based motion tracking using the optical flow and matrix operation performed based on the optical flow data and the sequence of images/video...and dense optical flow which tracks all frame pixels without selecting any features..)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki/Kanno’s parallel training of a neural network model on multiple graphics processing units, to include a means said ,... wherein the first optical flow data and the second optical flow data includes dense optical flow data.. AS TAUGHT BY Mahmoudi, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Claim 8, Haruki and Kanno and Mahmoudi further teach: the graphics multiprocessor configured to perform operations associated with the first subnetwork and the second subnetwork, the operations cause the graphics multiprocessor to: (is/are fully incorporated similar subject of claim(s) 1-2 cited above) and further in view of the following: determine velocities of moving objects of the first subject and the second subject; and assign a hazard associated with the moving objects to the first set of stationary objects and the second set of stationary objects based on a velocity and trajectory of the moving objects, (In Mahmoudi Pages 131 -146 begin section 4. “Motion tracking algorithm”, discloses the GPUs-based motion tracking using the optical flow and matrix operation performed based on the optical flow data and the sequence of images/video...and dense optical flow which tracks all frame pixels without selecting any features.. wherein the velocities of moving objects of the subject(s) assign a hazard associated with the moving objects to the stationary objects (static objects like trees and building and abnormal event(s) (i.e. hazardous) ... based on a velocity and trajectory of the moving objects... which are identified...)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki/Kanno’s parallel training of a neural network model on multiple graphics processing units, to include a means said ,... determine velocities of moving objects of the first subject and the second subject; and assign a hazard associated with the moving objects to the first set of stationary objects and the second set of stationary objects based on a velocity and trajectory of the moving objects ... AS TAUGHT BY Mahmoudi, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Regarding Claim(s) 13-16 (respectively) is/are fully incorporated similar subject of claim(s) 5-8 (respectively) cited above.
Response to Arguments
Applicant's arguments filed with this paper have been considered but are not persuasive (See the above rejection for details) and further view of the following:
Regarding the 103 rejections to 1-4, 9-12 and 17-20 rejected under 35 U.S.C. 103 as being unpatentable over Haruki and Kanno,
Applicant asserts Kanno does not disclose, the graphics multiprocessor configured to: load weights associated with a baseline set of layers of the modular neural network to the memory device;... load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment, wherein the first image includes the first subject…because, Kanno’s “hierarchal DNN” architecture, is a sequential processing chain, wherein the hierarch is a serail pipeline across devices…, which is not the same applicants’ claimed architecture…moreover, “Neither reference teaches loading a modular neural network comprising a baseline set of layers shared across multiple subject-specific subnetworks, determining a subject in a deployment environment, dynamically loading a subnetwork trained to recognize that subject, and performing inference on a captured image, all on a single graphics processing cluster..”…[Emphasis added] (see the remarks paged 13/18 second and third paragraphs).
For purposes of responding to Applicants’ remarks, the examiner will assume that Applicants is/are arguing for the patentability of Claim 1.
The examiner disagrees. In response to applicants’ argument(s) that the references fail to show certain features of applicant’s invention. As discussed in the Office Action dated 03/27/2026 and further in view of the O.A. as cited herein,
Haruki teaches: A graphics processor comprising: a memory device; a graphics processing cluster coupled with the memory device, the graphics processing cluster including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors interconnected via a data interconnect, wherein a graphics multiprocessor of the plurality of graphics multiprocessors, (In Haruki the Abstract and Para(s) 2 and 5, i.e., parallel training of a neural network model on multiple graphics processing units; wherein the Artificial neural networks (ANNs) generally include sets of adaptive weights,… Haruki further teaches: the circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject, moreover, Haruki in Para(s) 3-4 and 18-20, discloses the layers in convolutional neural networks usually start with convolutional layers having a small number of parameters and end with fully connected layers having a large number of parameters.... It is noted; the convolutional networks have wide applications in image and video recognition. Also, Haruki further teaches determine a first subject associated with a deployment environment; …wherein the deep learning using a convolutional neural network is an effective tool for solving complex problems in computer vision, speech recognition, and natural language processing. For example, deep learning has been successfully used to recognize objects in digital images....)
It is noted that Haruki discloses the circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject… efficient parallel training of a neural network model on multiple graphics processing units; wherein the training module collects gradients of multiple layers during backpropagation of training from a plurality of graphics processing units (GPUs),...
However, Haruki does not expressly teach, But the combination of Haruki and Kanno teach,... the graphics multiprocessor configured to: load weights associated with a baseline set of layers of the modular neural network to the memory device;... load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment, wherein the first image includes the first subject...(In Kanno the Abstract and Para(s) 21-24, describing a method where a unit that performs an operation on data of a second layer using data of a first layer and performs an operation on data of the first layer using data of the second layer in a multi-layered neural network . It is the weight data of deciding a relation between each piece of data of the first layer and each piece of data of the second layer in both the operations ... and the weight data is stored in one storage holding unit as all weight coefficient matrices to be constructed. Further, an operation unit including product-sum operators which are constituent elements of the weight coefficient matrix and correspond to operations of matrix elements in a one-to-one manner is provided, and when the matrix elements constituting the weight coefficient matrix are stored in the storage holding unit, the matrix elements are stored using a row vector of the matrix as a basic unit, and the operation of the weight coefficient matrix is performed in basic units in which the storage is performed in the storage holding unit...Moreover, in a case in which the data of the first layer is calculated from the data of the second layer using the weight coefficient matrix, the data of the second layer is arranged similarly to the column vector of the matrix, and each element is input to the product-sum operator, at the same time, a first row of the weight coefficient matrix is input to the product-sum operator, a multiplication operation related to both pieces of data is performed, and an operation result is stored in the accumulator, when second or less rows of the weight coefficient matrix are calculated, the data of the second layer is shifted to the left or the right each time a row operation of the weight matrix is performed, and then a multiplication operation of element data of a corresponding row of the weight coefficient matrix and the arranged data of the second layer is performed, then, data stored in the accumulator of the same operation unit is added, and a similar operation is performed up to an N-th row of the weight coefficient matrix is provide ... Also, in Kanno Para(s) 138-141, further mentions the same target is imaged through a plurality of cameras, and an image recognition process is executed. Since a video captured by a camera 1 and a video captured by a camera 2 differ in position, the shapes of the subject are different although the same subject is imaged. Therefore, it is efficient since it is possible to acquire information at the same time under different conditions such as a photographing angle or a radiation degree of light rays and perform the recognition and the learning...utilizing the weight coefficient matrix is input to the product-sum operator as describes in Para(s) 21-24., …)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki’s parallel training of a neural network model on multiple graphics processing units, to include a means said the graphics multiprocessor configured to: load weights associated with a baseline set of layers of the modular neural network to the memory device;... load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment, wherein the first image includes the first subject AS TAUGHT BY Kanno, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Accordingly, Haruki and Kanno clearly teach all the limitations of claim 1. In addition, to be nonobvious, an improvement must be "more than the predictable use of prior art elements according to their established functions." Id. at 1740. In addition, the Federal Circuit recently recognized that "[a]n obviousness determination is not the result of a rigid formula disassociated from the consideration of the facts of a case. Indeed, the common sense of those skilled in the art demonstrates why some combinations would have been obvious where others would not." Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161 (Fed. Cir. 2007) (citing KSR, 127 S. Ct. at 1739). The Federal Circuit relied in part on the fact that Leapfrog had presented no evidence that the inclusion of a reader in the combined device was "uniquely challenging or difficult for one of ordinary skill in the art" or "represented an unobvious step over the prior art." Id. at 1162 (citing KSR, 127 S. Ct. at 1741).
Also, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Merck & Co., Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986). The test for obviousness is what the combined teachings of the references would have suggested to one of ordinary skill in the art. See In re Kahn, 441 F.3d at 987-988; In re Young, 927 F.2d 588, 591 (Fed. Cir. 1991); and In re Keller, 642 F.2d 413,425 (CCPA 1981).
Moreover, in evaluating such references it is proper to take into account not only the specific teachings of the references but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. In re Preda, 401 F.2d 825, 826 (CCPA 1968).
In this case, as previously presented in the office action mailed 03/27/2026 in view of the above O.A. , Haruki in view of Kanno in the BRI disclose every limitation of Claim(s) 1-4, 9-12 and 17-20 and provide proper reasons to combine, in light of the above. Accordingly, for at least all the above evidence, therefore the Examiner respectfully maintains the rejection of claim(s) 1-4, 9-12 and 17-20, at least at this time.
Regarding the 103 rejections to 5-8 and 13-16 rejected under 35 U.S.C. 103 as being unpatentable over Haruki and Kanno and (NPL)- MAHMOODI Pages 139-150,
Applicant asserts MAHMOODI does not disclose, “… load first optical flow data associated with a first sequence of images that includes the first image; and perform a third matrix operation associated with the first subnetwork to facilitate detection of a first set of stationary objects of the first subject, the third matrix operation performed based on the first optical flow data and the first sequence of images…” …[Emphasis added] (See the remarks pages 16-18).
For purposes of responding to Applicant’s remarks, the examiner will assume that Applicant is arguing for the patentability of Claim 5.
The examiner disagrees. In response to applicant's argument(s) that the references fail to show certain features of applicant’s invention. As discussed in the Office Action dated 03/27/2026 and further in view of the O.A. as cited herein, Mahmoudi Pages 139 -146 begin section 4. “Motion tracking algorithm”, discloses the GPUs-based motion tracking using the optical flow and matrix operation performed based on the optical flow data and the sequence of images/video.( set of stationary objects such as located on the static objects like trees or a building... .)
Accordingly, it would have been obvious to one having ordinary skill in the art at the time before the effective filing date of the claimed invention was made to modify Haruki/Kanno’s parallel training of a neural network model on multiple graphics processing units, to include a means said ,... load first optical flow data associated with a first sequence of images that includes the first image;... , the third matrix operation performed based on the first optical flow data and the first sequence of images.. AS TAUGHT BY Mahmoudi, provides an improvement in a recognition rate by a convolutional neural network in an image recognition field. The deep learning can be applied to a wide variety of devices from image recognition terminals for automatic driving to cloud computing for big data analysis...[In Kanno Para 2-4]. It is noted the KSR ruling recommends references directed to similar subject matter to be combined.
Accordingly, Haruki and Kanno in view of Mahmoud clearly teach all the limitations of claim 5. In addition, to be nonobvious, an improvement must be "more than the predictable use of prior art elements according to their established functions." Id. at 1740. In addition, the Federal Circuit recently recognized that "[a]n obviousness determination is not the result of a rigid formula disassociated from the consideration of the facts of a case. Indeed, the common sense of those skilled in the art demonstrates why some combinations would have been obvious where others would not." Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161 (Fed. Cir. 2007) (citing KSR, 127 S. Ct. at 1739). The Federal Circuit relied in part on the fact that Leapfrog had presented no evidence that the inclusion of a reader in the combined device was "uniquely challenging or difficult for one of ordinary skill in the art" or "represented an unobvious step over the prior art." Id. at 1162 (citing KSR, 127 S. Ct. at 1741).
Also, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Merck & Co., Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986). The test for obviousness is what the combined teachings of the references would have suggested to one of ordinary skill in the art. See In re Kahn, 441 F.3d at 987-988; In re Young, 927 F.2d 588, 591 (Fed. Cir. 1991); and In re Keller, 642 F.2d 413,425 (CCPA 1981).
Moreover, in evaluating such references it is proper to take into account not only the specific teachings of the references but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. In re Preda, 401 F.2d 825, 826 (CCPA 1968).
In this case, as previously presented in the office action mailed 03/27/2026 in view of the above O.A. , Haruki and Kanno in view of Mahmoud in the BRI disclose every limitation of Claim(s) 5-8 and 13-16 and provide proper reasons to combine, in light of the above. Accordingly, for at least all the above evidence, therefore the Examiner respectfully maintains the rejection of claim(s) 5-8 and 13-16, at least at this time.
Accordingly, for at least all the above evidence, therefore the Examiner respectfully maintains the rejection of claim(s) 1-20, at least at this time.
Regarding the 101 rejection. Applicant argues, that claims 1-20 rejection under 35 U.S.C. § 101 as being direct to nonpatentable subject matter should be “traversed” (see the remarks pages 9-11 ) because: “… performing matrix operations on captured images within that architecture. This is an improvement to the functioning of the graphics processor itself, not a mere implementation of an abstract idea on a computer...” and..” use the applicant's own background section as evidence that the claimed invention is conventional and then dismiss the claimed invention as generic on that basis….and the claimed architecture is not a generic computer arrangement. It is a specific structural configuration…This arrangement solves the technical problem of efficiently performing multi-subject computer vision on GPU hardware by sharing computation across common layers while maintaining subject-specific specialization. It is an improvement to the functioning of the graphics processor itself, not a mere implementation of an abstract idea on a computer …” and “At Step 2B, the Office Action's analysis is not a separate analysis; it restates the Prong Two conclusion verbatim and reaches the same conclusion based on the same additional elements ...”…[Emphasis added]
The examiner disagrees. As discussed in the Office Action dated 03/27/2026 and further in view of the O.A. as cited herein, claims 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter) includes... “a graphics processor comprising: a memory device; a graphics processing cluster coupled with the memory device, the graphics processing cluster including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors “interconnected via a data interconnect”, … “load weights for a first subnetwork to the memory device”, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image “captured within the deployment environment”, wherein the first image includes the first subject ...and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recite ... ... “a graphics processor comprising: a memory device; a graphics processing cluster coupled with the memory device, the graphics processing cluster including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors “interconnected via a data interconnect”, wherein a graphics multiprocessor of the plurality of graphics multiprocessors includes circuitry” configured to “load a modular neural network” including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject, the “graphics multiprocessor” configured to: “ load weights associated with a baseline set of layers of the modular neural network” to the memory device; determine a first subject associated with a deployment environment; “load weights for a first subnetwork to the memory device”, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image “captured within the deployment environment”, wherein the first image includes the first subject... ...These limitation(s) recite mathematical calculation; since the training of the machine model; to recognize the first subject; and perform a first matrix operation...then “Apply it” (i.e. load weights associated with a baseline set of layers of the modular neural network...) (as describes in US 20230334316 A1. Para 147; i.e., the machine learning application can be configured to perform the necessary computations using the primitives provided by the machine learning framework ...which are computational operations that are performed while training a convolutional neural network (CNN). The machine learning framework can also provide primitives to implement basic linear algebra subprograms performed by many machine-learning algorithms, such as matrix and vector operation...)..., “APPLY IT” (the weights associated with a baseline set of layers of the modular neural network” to the memory device). Thus, independent claim(s) 1, 9 and 17 recite abstract idea and "covers performance of the Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations” .
Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as: ... “graphics multiprocessor/ memory device ” ... to: load weights associated with a baseline set of layers of the modular neural network” to the memory device; that determine a first subject associated with a deployment environment; and“ load weights for a first subnetwork to the memory device, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment......it is noted, the improvement in the abstract idea itself ... but do not integrate the judicial exception into a practical application (In the BRI (Broadest Reasonable Interpretations) in light of the current specifications ( see the US 20230334316 A1. Para 3 ; i.e., Parallel graphics processors with single instruction, multiple thread (SIMT) architectures are designed to maximize the amount of parallel processing in the graphics pipeline. In an SIMT architecture, groups of parallel threads attempt to execute program instructions synchronously together as often as possible to increase processing efficiency. The efficiency provided by parallel machine learning algorithm implementations allows the use of high-capacity networks and enables those networks to be trained on larger datasets) ... to load weights for a first subnetwork to the memory device, the first subnetwork trained to recognize the first subject; and perform a first matrix operation...These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) such as ... ... “graphics multiprocessor/ memory device ” to: load weights associated with a baseline set of layers of the modular neural network” to the memory device; that determine a first subject associated with a deployment environment; and“ load weights for a first subnetwork to the memory device, the first subnetwork “trained to recognize the first subject; and perform a first matrix operation” associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment...These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.04(d) and 2106.05(f)).
As to the dependent claim(s) 2-8, 10-16 and 18-20, further recite, addition limitation(s) such as, (dynamically load weights for a second subnetwork, perform a second matrix operation, second image includes the second subject, output associated with the baseline set of layers of the modular neural network is provided as input the first subnetwork to perform the first computer vision operation and provided as input the second subnetwork to perform the second computer vision operation, apply a first priority to the first subnetwork; apply a second priority to the second subnetwork; and adjust a resource allocation within the graphics processing cluster based on the first priority and the second priority, load first optical flow data associated with a first sequence of images, perform a fourth matrix operation, facilitate detection of a second set of stationary objects of the second subject, second optical flow data includes dense optical flow data, determine velocities of moving objects, assign a hazard associated with the moving objects and second set of stationary objects based on a velocity and trajectory of the moving objects...etc.). These limitation(s) only amount to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Thus, in light of the Advance notice of change to the MPEP in light of Ex Parte Desjardins new (December 5, 2025) and the memorandum dated August 4, 2025 and December 4, 2025...(i.e. Examples of claims that improve technology or a technical field and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (data structure claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253, 125960, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea);
Also, After the examiner has consulted the specification and determined that the disclosed invention improves technology or a technical field, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp.,838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues)....The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology or a technical field (e.g., the improvement described in the specification). [MPEP § 2106.05(a) Fourth and Fifth Para(s)] . See also Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential) (“Examiners and panels should not evaluate claims at such a high level of generality” that potentially meaningful technical limitations).
An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.... (See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025) (Appeals Review Panel Decision)re dismissed without adequate explanation).
In this case, in Step 2A and 2B Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “graphics multiprocessor/ memory device ” ... to load weights associated with a baseline set of layers of the modular neural network”… associated with the first subnetwork to facilitate a first computer vision operation on a first image captured within the deployment environment...
Thes limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Accordingly, claims 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101... for at least all the above evidence claims 1-20 remain rejected. (The examiner can normally be reached Monday - Friday 9-5 EST for Examiner interviews are available via telephone).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUOC A TRAN whose telephone number is (571)272-8664. The examiner can normally be reached Monday-Friday 9am-5pm EST.
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/QUOC A TRAN/Primary Examiner, Art Unit 2145
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